Identification of interconnected markers for T-cell acute lymphoblastic leukemia

Abstract

T-cell acute lymphoblastic leukemia (T-ALL) is a complex disease, resulting from proliferation of differentially arrested immature T cells. The molecular mechanisms and the genes involved in the generation of T-ALL remain largely undefined. In this study, we propose a set of genes to differentiate individuals with T-ALL from the nonleukemia/healthy ones and genes that are not differential themselves but interconnected with highly differentially expressed ones. We provide new suggestions for pathways involved in the cause of T-ALL and show that network-based classification techniques produce fewer genes with more meaningful and successful results than expression-based approaches. We have identified 19 significant subnetworks, containing 102 genes. The classification/prediction accuracies of subnetworks are considerably high, as high as 98%. Subnetworks contain 6 nondifferentially expressed genes, which could potentially participate in pathogenesis of T-ALL. Although these genes are not differential, they may serve as biomarkers if their loss/gain of function contributes to generation of T-ALL via SNPs. We conclude that transcription factors, zinc-ion-binding proteins, and tyrosine kinases are the important protein families to trigger T-ALL. These potential disease-causing genes in our subnetworks may serve as biomarkers, alternative to the traditional ones used for the diagnosis of T-ALL, and help understand the pathogenesis of the disease.

Figure 1

First seven of the most frequent subnetworks. Nodes represent proteins, and edges represent interactions. The color of each node ranges in accordance with the change in expression of the corresponding gene for T-ALL versus healthy samples. The shape of each node shows whether its gene is significantly differentially expressed (diamond; P < 0.05 from a two-tailed t-test) or not (circle).

Figure 2

Second six of the most frequent subnetworks. Nodes represent proteins, and edges represent interactions. The color of each node ranges in accordance with the change in expression of the corresponding gene for T-ALL versus healthy samples. The shape of each node shows whether its gene is significantly differentially expressed (diamond; P < 0.05 from a two-tailed t-test) or not (circle).

Figure 3

The last six of the most frequent subnetworks. Nodes represent proteins, and edges represent interactions. The color of each node ranges in accordance with the change in expression of the corresponding gene for T-ALL versus healthy samples. The shape of each node shows whether its gene is significantly differentially expressed (diamond; P < 0.05 from a two-tailed t-test) or not (circle).

Figure 4

The heat map shows the hierarchical clustering result of the 100 most differentially expressed genes in T-ALL with respect to healthy individuals. Red and green spots represent upregulated and downregulated genes, respectively. Black spots denote equal expression. The columns labeled with light green belong to healthy individuals, and the columns labeled with black are individuals with T-ALL.

Figure 5

The heat map shows the hierarchical clustering result of the 200 most differentially expressed genes in T-ALL with respect to healthy individuals. Red and green spots represent up-regulated and down-regulated genes, respectively. Black spots denote equal expression. The columns labeled with light green belong to healthy individuals, and the columns labeled with black are individuals with T-ALL.